The recent advancements in depth estimation and 3D imaging have shown significant progress, particularly in leveraging novel data sources and computational techniques to enhance accuracy and consistency. A notable trend is the integration of multiple modalities, such as audio and visual data, to improve metric depth estimation, addressing the limitations of single-modality approaches. Additionally, the use of lightweight models and efficient training strategies has enabled real-time processing, crucial for applications like autonomous driving and augmented reality. The field is also witnessing a shift towards more robust methods that can handle dynamic scenes and varying illumination conditions, with dual-exposure techniques and motion-aware networks emerging as promising solutions. Furthermore, the development of synthetic datasets and the open-sourcing of models and data are fostering a collaborative environment, accelerating innovation and benchmarking. Notably, the introduction of models that can independently learn temporal consistency in static and dynamic areas without additional information marks a significant leap forward in video depth estimation. These developments collectively point towards a future where depth estimation technologies are not only more accurate and efficient but also more versatile and adaptable to diverse real-world scenarios.